CrewAI
Python framework for building teams of role-playing AI agents that work together on tasks.
- Stars
- 60.4k
- Forks
- 9k
- Licence
- MIT
- Language
- Python
- Last update
- 4 days ago
- Type
- Library
What it is
CrewAI is an open-source Python framework for orchestrating multiple AI agents. You give each agent a role and goal, and they collaborate through what the project calls Crews, while Flows provide event-driven, more precisely controlled workflows. It is aimed at developers building multi-step automations with LLMs.
- Build a crew of agents that research and write content together
- Create an event-driven Flow that chains LLM calls and Crews into an automation
- Add structured output (Pydantic or JSON) to agent tasks
- Connect agents to tools and LLMs of your choice
Great for
- Python developers prototyping multi-agent workflows
- Teams that want role-based agents collaborating on a task
- Developers who want both autonomous agent teams and tighter step-by-step control via Flows
- Builders who want low-level control over prompts and execution logic
Not ideal for
- Non-programmers, since the project is a Python framework and requires writing code
- Projects needing a fully managed hosted product without setup; the README's commercial AMP Suite is a separate paid offering
- Simple single-prompt tasks that do not need multiple agents or workflow control
How to get it
- 1
Install with a package manager
You'll need: Python
Install CrewAI (from PyPI, per the project's PyPI badge)
pip install crewai - 2
Run it
- Install the crewai package with pip
- Follow the Getting Started tutorial linked in the README
- Define agents and tasks in a Crew, then run it
- Optionally wrap steps in a Flow for finer control
- 3
Get the most out of it
- Use the official docs at docs.crewai.com for current API details
- Install the CrewAI Skills package so coding agents follow CrewAI patterns
- Use structured output options like output_pydantic for reliable results
- Explore the Flows and Crews together for hybrid workflows
Downloads
Strengths
- Open source under the MIT license with an active repository
- Offers both autonomous Crews and deterministic event-driven Flows
- Includes documentation, courses, and an official skills package for coding agents
Trade-offs
- The README's performance and 'production-ready' claims are promotional and not backed by benchmarks in the excerpt
- Core concepts (Crews, Flows, Agents) add a learning curve for newcomers
- Some advanced features and a control plane are part of a commercial suite
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| crewAI (this) | 60.4k | Library | intermediate | 4 days ago | Python developers prototyping multi-agent workflows |
| langgraph | 44.3k | Library | intermediate | 4 days ago | Python developers building multi-step AI agents that must survive failures or long waits |
| llama_index | 52.8k | Library | intermediate | 10 days ago | Python developers building retrieval-augmented generation (RAG) apps over private documents |
| semantic-kernel | 28.7k | Library | intermediate | 4 days ago | Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents |
| Prompt-Engineering-Guide | 79.8k | Learning resource | beginner | 8 months ago | People who are new to prompting and want structured lessons with examples |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.